What is behavior based detection and security
Behavior-based security and detection is part of a next-generation, multi-layered approach to protection. It's one of the best ways to protect against advanced threats like ransomware, zero-day malware, and malware that doesn't leave a file behind.
When we talk about behavior-based security, what we are ultimately pointing to are the hazards posed by insiders and how to mitigate them. This is because behavior-based threat detection is based on how people behave. In other words, our very own employees pose the greatest risk to the assets of our company, whether they do it through carelessness or with malicious intent.
Traditional security is signature based, it watches data streams and compares data in transit to signatures in an anti-virus vendor’s library of known threats. Behavior-based security programs work a little differently. They also watch data streams, but then they compare the activity of the data streams to a baseline of normal behavior and look for differences. Behavior-based security products use applied math and machine learning to flag events that are statistically important.
Even though there may still be times when an organization needs to choose between signature-based and anomaly-based security software, there are many intrusion detection and prevention products that use both methods. In this post I will let you know everything about behavior based malware detection and security inside an organization.
Behavior based malware detection
Behavior-based security is a proactive approach to security in which all relevant activity is observed so that deviations from regular behavior patterns can be discovered and dealt with as promptly as possible. This approach to security is known as the “two-factor authentication” method. It is anticipated that this method of managing security will play a significant part in the process of safeguarding computers at the edge of the network as machine learning continues to make strides toward improvement.
Behavior-based malware detection looks at an object based on what it wants to do before it does that thing. Behavior or possible behavior of an object is looked at to see if it does anything suspicious. If an object tries to do things that are clearly wrong or not allowed, that would be a sign that it is malicious or at least suspicious.
There are many things that could mean someone is in danger. Some examples are trying to find a sandbox environment, turning off security controls, installing rootkits, or signing up for autostart.
Dynamic analysis is the process of looking for bad behavior as the program runs. Static analysis can also tell if an object is a threat or has bad intentions by looking for dangerous features in its code and structure.
Even though there is no perfect solution, behavior-based detection is still the best way for technology to find new and unknown threats in close to real time. Here are some situations where behavior-based technology works but signature-based systems don’t:
- Protecting against types of malware attacks that no one has ever thought of
- Malware that is made to attack a specific person or organization can be found by
- Figuring out what the malware does when files are opened in a certain setting
- Getting as much information as possible about the malware
There are some important things to keep in mind. Malware that knows it’s running in a sandbox will try to avoid being found by slowing down malicious actions. It’s important that a sandbox can’t be found, but most of them can’t.
Analysis of an object’s behavior also takes time. Static analysis can be done in real time, but dynamic analysis may cause a delay as the object is used. Also, many behavior solutions only work in the cloud, which may be a problem for some businesses.
Behavior based security software
Behavior-based security software is built with built-in intelligence that looks for deviations from malware signatures and can tell if files that are sent to a network or system could be dangerous. This is a good way to protect end-user devices, network elements, and servers from malicious or even potentially malicious actions.
In behavior-based detection, the software is set up to look at every single line of code and evaluate it. It also looks at all the possible things that line of code could do, like give access to important or unimportant files, processes, or internal services. This analysis also looks at how OS-level instructions and low-level code from a rootkit are run. The software tries to find all malicious or potentially malicious actions that could cause harm and alerts the right people so they can take the right steps.
Malicious behavior can have many different sides that need to be looked at. This includes products like intrusion detection based on behavior, threat analysis based on behavior, and user behavior analytics. Most programs that are based on behavior use a control system that is based on policies. Based on the vendor’s knowledge and experience, there is a standard set of policies that helps define the behaviors that can be allowed to happen. This software also lets administrators make or change policies to allow or disallow certain requirements that may be unique to their organization or industry.
Most behavioral detection solutions use advanced technologies like machine learning, an advanced correlation engine, and behavioral biometrics to map out typical malicious actions like installing a rootkit, trying to find out what the sandbox environment is, or trying to turn off security controls.
Behaviors that should to monitored inside the organization
Here are some examples of the kinds of actions we need to watch out for to solve this problem:
- An employee sending sensitive information by email to their own account.
- An employee accessing sensitive information or asking to use resources that don’t have anything to do with their job.
- A former worker who looks at private information after their contract has ended.
- A worker who tried to get around security controls.
- A worker copies a lot of information to an external drive or a container in the cloud.
- An employee connecting to the network from a place and with a device that are not known.
- A worker who gets into company information outside of normal work hours.
In addition to the aforementioned, the likelihood that an employee or former employee will decide to steal sensitive data increases if a company is forced to lay off a large number of employees within a short period of time for whatever reason. This circumstance increases the likelihood that an employee will steal sensitive data. Before their access is permanently removed, an employee who is on the verge of leaving their position may in some instances establish a new account with new credentials.
As you can see, there are a lot of different ways that employees with malevolent intent or carelessness on their part can put the security of your network at risk. As a result, it is imperative that we have protections in place, one of which is the practice of being proactive in monitoring and responding to threats posed by insiders.
Quick tips for implementing behavior-based security
Every industry and organization is different and has its own ideas about what security means and how it should work. For example, in some financial organizations, it may be normal to have two or three failed attempts, but in critical industries like energy or oil, it may be alarming to have only one failed attempt.
Because of how behavior-based security works, it is almost certain that you will need to tune it before you can use it in your environment. You can’t just open the box and expect your business needs to be met. Initial baselines set by the vendor and recommendations from leaders in your industry can help you get started, but in the end, you will need to do the fine-tuning to make behavior-based security software work best for your organization.
When putting behavior-based security into place, organizations need to think about the following:
- Collect all possible data: When there is more data to look at, the machine learning-based system will be better able to spot unusual things.
- Start early: Don’t wait for a tipping point or a major event to happen before looking into all the different ways behavior-based security could be used. Before making a real investment, try out the different options to learn more about them.
- Make use of the best tools: There are both free and paid tools on the market that can help with machine learning. Explore them and choose the one for which your organization has the right mix of skills, compatibility, and money.
- Self-improvement: It is important because criminals are always trying new and different ways to break into networks they are trying to break into. So, you also need to keep looking at and improving your behavior-based security policies to find and stop these enemies.
How to prevent from insider threat
When it comes to dealing with dangers posed by insiders, one of the problems that we face is that every security risk could be interpreted as the consequence of careless or malevolent behavior on the part of insiders. As a consequence of this, explaining how to prevent insider threats is effectively the same as saying “How to prevent security threats,” which is ultimately outside the purview of this article. As an illustration, we might go through topics such as acceptable use regulations, desktop security, network segmentation, and the implementation of multi-factor authentication in order to stop illegal access.
We may also discuss the significance of having appropriate physical security measures in place, such as locks, alarms, badges, surveillance cameras, biometric authentication, and so on. To keep things straightforward, we could say that mitigating insider threats really comes down to two things: training and monitoring. This would be a simplification of the situation, but it would make the point.
Flaws of behavior-based security
A static analysis method is used for signature-based detection, which can be done in real time. But behavior-based security is not like this. When you do a dynamic analysis across multiple dimensions, you add some latency, which hurts the performance.
Also, some types of malware first try to figure out if they are running in a sandbox. With an anti-sandboxing technique, this kind of malware might be able to avoid being found by stopping it from doing anything bad. Also, some behavior-based security solutions only work in the cloud, which might not work well with policies and compliance standards.
Using the right mix of signature-based security and behavior-based security
Both signature-based and behavior-based techniques for finding malware have their own pros and cons. Using the right mix of the two helps organizations get a higher level of security. For example, behavior-based security can help avoid any new zero-day malware threat. However, a quick look back of relevant parameters (indicators of compromise) into the existing signature-based firewall and anti-malware software can instantly help stop massive floods or waves of these attacks, adding extra layers of security to the networks. You can get the best security by using the right mix of both technologies.
Cybersecurity assaults on large public and private infrastructures in the US and the rest of the world cost millions of dollars in damages and ransoms, not to mention a soiled reputation and lost data that could hurt the firm long-term. Although these assaults have become more complicated and rely on new zero-day exploits, most may be mitigated if not prevented by utilizing a competent threat defense program with predictive security. A security software provider claimed and demonstrated that their solution could have prevented a recent complicated attack, even in 2015 and offline (e.g., on an isolated segmented network). The “magic” of next-generation security is that you can detect and avoid emerging dangers instead of waiting for them to happen and undertaking substantial damage control.
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